Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations532825
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory56.9 MiB
Average record size in memory112.0 B

Variable types

Numeric11
Categorical3

Alerts

accnt_bgn_date is highly overall correlated with accnt_bgn_yearHigh correlation
accnt_bgn_year is highly overall correlated with accnt_bgn_dateHigh correlation
cprtn_prd_d is highly overall correlated with erly_pnsn_flgHigh correlation
erly_pnsn_flg is highly overall correlated with cprtn_prd_d and 2 other fieldsHigh correlation
gndr is highly overall correlated with pnsn_ageHigh correlation
pnsn_age is highly overall correlated with erly_pnsn_flg and 1 other fieldsHigh correlation
prsnt_age is highly overall correlated with erly_pnsn_flgHigh correlation
erly_pnsn_flg is highly imbalanced (77.5%) Imbalance

Reproduction

Analysis started2024-10-27 06:21:14.563044
Analysis finished2024-10-27 06:22:01.579988
Duration47.02 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

location
Real number (ℝ)

Distinct164734
Distinct (%)30.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.036482345
Minimum7.2878831 × 10-5
Maximum0.93116904
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-10-27T06:22:01.714652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7.2878831 × 10-5
5-th percentile0.0009324753
Q10.010909121
median0.026499609
Q30.041558501
95-th percentile0.10502271
Maximum0.93116904
Range0.93109616
Interquartile range (IQR)0.030649379

Descriptive statistics

Standard deviation0.049598478
Coefficient of variation (CV)1.35952
Kurtosis46.619598
Mean0.036482345
Median Absolute Deviation (MAD)0.015355228
Skewness5.4828531
Sum19438.706
Variance0.002460009
MonotonicityNot monotonic
2024-10-27T06:22:01.930139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03636653686 36204
 
6.8%
0.01818326843 18178
 
3.4%
0.01212217895 12620
 
2.4%
0.009091634214 9648
 
1.8%
0.007273307371 7786
 
1.5%
0.006061089476 6484
 
1.2%
0.005195219551 5443
 
1.0%
0.004545817107 4680
 
0.9%
0.004040726317 4096
 
0.8%
0.003636653686 3619
 
0.7%
Other values (164724) 424067
79.6%
ValueCountFrequency (%)
7.287883137 × 10-51
< 0.1%
7.302517441 × 10-51
< 0.1%
7.317210635 × 10-51
< 0.1%
7.331963076 × 10-51
< 0.1%
7.346775122 × 10-51
< 0.1%
7.361647137 × 10-51
< 0.1%
7.376579484 × 10-51
< 0.1%
7.391572532 × 10-51
< 0.1%
7.406626651 × 10-51
< 0.1%
7.421742215 × 10-52
< 0.1%
ValueCountFrequency (%)
0.9311690383 1
 
< 0.1%
0.925874349 1
 
< 0.1%
0.9196972114 1
 
< 0.1%
0.9123969579 1
 
< 0.1%
0.9036366537 1
 
< 0.1%
0.8929296152 2
 
< 0.1%
0.8795458171 2
 
< 0.1%
0.8623380767 2
 
< 0.1%
0.8393944228 4
 
< 0.1%
0.8072733074 10
< 0.1%

addrss_type
Real number (ℝ)

Distinct532802
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.036614107
Minimum0.00079057689
Maximum0.25909163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-10-27T06:22:02.148222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.00079057689
5-th percentile0.030423404
Q10.030540759
median0.030608328
Q30.03105949
95-th percentile0.064708336
Maximum0.25909163
Range0.25830106
Interquartile range (IQR)0.00051873071

Descriptive statistics

Standard deviation0.013085451
Coefficient of variation (CV)0.35738823
Kurtosis1.7736994
Mean0.036614107
Median Absolute Deviation (MAD)0.00012706598
Skewness1.7616708
Sum19508.912
Variance0.00017122902
MonotonicityNot monotonic
2024-10-27T06:22:02.375287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03636653686 4
 
< 0.1%
0.01212217895 4
 
< 0.1%
0.01818326843 4
 
< 0.1%
0.003636653686 2
 
< 0.1%
0.1272729086 2
 
< 0.1%
0.1131314743 2
 
< 0.1%
0.002797425912 2
 
< 0.1%
0.003030544738 2
 
< 0.1%
0.003306048805 2
 
< 0.1%
0.1197862669 2
 
< 0.1%
Other values (532792) 532799
> 99.9%
ValueCountFrequency (%)
0.0007905768882 1
< 0.1%
0.0008081452635 1
< 0.1%
0.0008265122013 1
< 0.1%
0.0008457334152 1
< 0.1%
0.0008658699251 1
< 0.1%
0.0008869887038 1
< 0.1%
0.0009091634214 1
< 0.1%
0.000932475304 1
< 0.1%
0.0009570141278 1
< 0.1%
0.0009828793745 1
< 0.1%
ValueCountFrequency (%)
0.2590916342 1
< 0.1%
0.2072733074 1
< 0.1%
0.2036366537 1
< 0.1%
0.1851242306 1
< 0.1%
0.1727277561 1
< 0.1%
0.1696972114 1
< 0.1%
0.1614546615 1
< 0.1%
0.1598087651 1
< 0.1%
0.1566435798 1
< 0.1%
0.1552448668 1
< 0.1%

prvs_npf
Real number (ℝ)

Distinct522060
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.036455654
Minimum0.00018842765
Maximum0.7818185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-10-27T06:22:02.764560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.00018842765
5-th percentile0.025398991
Q10.025536612
median0.025699073
Q30.027377749
95-th percentile0.064632776
Maximum0.7818185
Range0.78163008
Interquartile range (IQR)0.0018411373

Descriptive statistics

Standard deviation0.038510279
Coefficient of variation (CV)1.0563596
Kurtosis53.060861
Mean0.036455654
Median Absolute Deviation (MAD)0.00027316601
Skewness6.6112815
Sum19424.484
Variance0.0014830416
MonotonicityNot monotonic
2024-10-27T06:22:02.995219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03636653686 184
 
< 0.1%
0.01818326843 140
 
< 0.1%
0.01212217895 118
 
< 0.1%
0.009091634214 99
 
< 0.1%
0.007273307371 89
 
< 0.1%
0.006061089476 83
 
< 0.1%
0.005195219551 72
 
< 0.1%
0.004545817107 64
 
< 0.1%
0.004040726317 58
 
< 0.1%
0.003636653686 54
 
< 0.1%
Other values (522050) 531864
99.8%
ValueCountFrequency (%)
0.0001884276521 1
< 0.1%
0.0001894090461 1
< 0.1%
0.0001904007165 1
< 0.1%
0.0001914028256 2
< 0.1%
0.0001924155389 2
< 0.1%
0.0001934390258 2
< 0.1%
0.0001944734591 2
< 0.1%
0.0001955190154 2
< 0.1%
0.0001965758749 2
< 0.1%
0.000197644222 2
< 0.1%
ValueCountFrequency (%)
0.7818185041 1
 
< 0.1%
0.7590916342 2
 
< 0.1%
0.7545458171 1
 
< 0.1%
0.7530305447 1
 
< 0.1%
0.7305787761 1
 
< 0.1%
0.7194809338 1
 
< 0.1%
0.7036366537 1
 
< 0.1%
0.6951051182 1
 
< 0.1%
0.6897729086 1
 
< 0.1%
0.6787888456 6
< 0.1%

brth_plc
Real number (ℝ)

Distinct23020
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03738081
Minimum5.3245296 × 10-5
Maximum0.93116904
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-10-27T06:22:03.230775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5.3245296 × 10-5
5-th percentile0.0020203632
Q10.018183268
median0.036366537
Q30.036366537
95-th percentile0.083499925
Maximum0.93116904
Range0.93111579
Interquartile range (IQR)0.018183268

Descriptive statistics

Standard deviation0.048824737
Coefficient of variation (CV)1.3061444
Kurtosis61.450947
Mean0.03738081
Median Absolute Deviation (MAD)0
Skewness6.8018222
Sum19917.43
Variance0.0023838549
MonotonicityNot monotonic
2024-10-27T06:22:03.448400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03636653686 309811
58.1%
0.01818326843 37382
 
7.0%
0.01212217895 17188
 
3.2%
0.009091634214 10763
 
2.0%
0.007273307371 7550
 
1.4%
0.006061089476 5772
 
1.1%
0.005195219551 4650
 
0.9%
0.004545817107 3854
 
0.7%
0.004040726317 3281
 
0.6%
0.003636653686 2825
 
0.5%
Other values (23010) 129749
24.4%
ValueCountFrequency (%)
5.324529554 × 10-51
< 0.1%
5.332336782 × 10-51
< 0.1%
5.340166939 × 10-51
< 0.1%
5.348020126 × 10-51
< 0.1%
5.355896444 × 10-51
< 0.1%
5.363795996 × 10-51
< 0.1%
5.371718886 × 10-51
< 0.1%
5.379665215 × 10-51
< 0.1%
5.38763509 × 10-51
< 0.1%
5.395628614 × 10-51
< 0.1%
ValueCountFrequency (%)
0.9311690383 1
 
< 0.1%
0.925874349 1
 
< 0.1%
0.9196972114 1
 
< 0.1%
0.9123969579 1
 
< 0.1%
0.9036366537 2
< 0.1%
0.8929296152 2
< 0.1%
0.8795458171 2
< 0.1%
0.8690911025 1
 
< 0.1%
0.8623380767 4
< 0.1%
0.8597404669 1
 
< 0.1%

okato
Real number (ℝ)

Distinct460459
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.036500988
Minimum7.2733074 × 10-5
Maximum0.67878885
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-10-27T06:22:03.665223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7.2733074 × 10-5
5-th percentile0.011484472
Q10.022463894
median0.030692252
Q30.043769427
95-th percentile0.083431622
Maximum0.67878885
Range0.67871611
Interquartile range (IQR)0.021305533

Descriptive statistics

Standard deviation0.022692711
Coefficient of variation (CV)0.62170128
Kurtosis21.411154
Mean0.036500988
Median Absolute Deviation (MAD)0.010589581
Skewness2.4236305
Sum19448.639
Variance0.00051495912
MonotonicityNot monotonic
2024-10-27T06:22:03.899810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03636653686 81
 
< 0.1%
0.01818326843 73
 
< 0.1%
0.01212217895 69
 
< 0.1%
0.009091634214 67
 
< 0.1%
0.007273307371 65
 
< 0.1%
0.006061089476 62
 
< 0.1%
0.005195219551 61
 
< 0.1%
0.004545817107 59
 
< 0.1%
0.004040726317 57
 
< 0.1%
0.003636653686 55
 
< 0.1%
Other values (460449) 532176
99.9%
ValueCountFrequency (%)
7.273307371 × 10-51
< 0.1%
7.287883137 × 10-51
< 0.1%
7.302517441 × 10-51
< 0.1%
7.317210635 × 10-51
< 0.1%
7.331963076 × 10-51
< 0.1%
7.346775122 × 10-51
< 0.1%
7.361647137 × 10-51
< 0.1%
7.376579484 × 10-51
< 0.1%
7.391572532 × 10-51
< 0.1%
7.406626651 × 10-51
< 0.1%
ValueCountFrequency (%)
0.6787888456 1
 
< 0.1%
0.576623791 1
 
< 0.1%
0.5487605943 1
 
< 0.1%
0.5198654273 1
 
< 0.1%
0.5181832684 7
< 0.1%
0.5090916342 2
 
< 0.1%
0.5060610895 1
 
< 0.1%
0.5045458171 1
 
< 0.1%
0.5036366537 1
 
< 0.1%
0.5030305447 1
 
< 0.1%

gndr
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.5 MiB
0
343320 
1
189505 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters532825
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 343320
64.4%
1 189505
35.6%

Length

2024-10-27T06:22:04.116027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-27T06:22:04.270133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 343320
64.4%
1 189505
35.6%

Most occurring characters

ValueCountFrequency (%)
0 343320
64.4%
1 189505
35.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 532825
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 343320
64.4%
1 189505
35.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 532825
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 343320
64.4%
1 189505
35.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 532825
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 343320
64.4%
1 189505
35.6%

lk
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.5 MiB
0
448437 
1
84388 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters532825
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 448437
84.2%
1 84388
 
15.8%

Length

2024-10-27T06:22:04.432266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-27T06:22:04.585688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 448437
84.2%
1 84388
 
15.8%

Most occurring characters

ValueCountFrequency (%)
0 448437
84.2%
1 84388
 
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 532825
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 448437
84.2%
1 84388
 
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 532825
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 448437
84.2%
1 84388
 
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 532825
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 448437
84.2%
1 84388
 
15.8%

accnt_bgn_date
Real number (ℝ)

High correlation 

Distinct4070
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2432191 × 1018
Minimum1.0933056 × 1018
Maximum1.7005248 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-10-27T06:22:04.756922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.0933056 × 1018
5-th percentile1.1291616 × 1018
Q11.1647584 × 1018
median1.2413952 × 1018
Q31.308528 × 1018
95-th percentile1.384992 × 1018
Maximum1.7005248 × 1018
Range6.072192 × 1017
Interquartile range (IQR)1.437696 × 1017

Descriptive statistics

Standard deviation8.8614196 × 1016
Coefficient of variation (CV)0.071278019
Kurtosis-0.68610523
Mean1.2432191 × 1018
Median Absolute Deviation (MAD)7.48224 × 1016
Skewness0.30804657
Sum-4.3471845 × 1018
Variance7.8524758 × 1033
MonotonicityNot monotonic
2024-10-27T06:22:04.979188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0965024 × 10186070
 
1.1%
1.2936672 × 10183788
 
0.7%
1.1359008 × 10183671
 
0.7%
1.1673504 × 10183590
 
0.7%
1.096416 × 10183320
 
0.6%
1.2937536 × 10182826
 
0.5%
1.1352096 × 10182708
 
0.5%
1.1343456 × 10182605
 
0.5%
1.1988864 × 10182456
 
0.5%
1.1527488 × 10182434
 
0.5%
Other values (4060) 499357
93.7%
ValueCountFrequency (%)
1.0933056 × 10182
 
< 0.1%
1.093392 × 10184
 
< 0.1%
1.0934784 × 101839
< 0.1%
1.0935648 × 10185
 
< 0.1%
1.093824 × 10186
 
< 0.1%
1.0939104 × 10186
 
< 0.1%
1.0939968 × 10183
 
< 0.1%
1.0940832 × 10181
 
< 0.1%
1.0941696 × 10185
 
< 0.1%
1.0944288 × 101814
 
< 0.1%
ValueCountFrequency (%)
1.7005248 × 10181
< 0.1%
1.6971552 × 10181
< 0.1%
1.6680384 × 10181
< 0.1%
1.6657056 × 10181
< 0.1%
1.663632 × 10181
< 0.1%
1.6375392 × 10181
< 0.1%
1.6354656 × 10181
< 0.1%
1.6255296 × 10181
< 0.1%
1.6215552 × 10181
< 0.1%
1.621296 × 10181
< 0.1%

pstl_code
Real number (ℝ)

Distinct29036
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean458406.58
Minimum0
Maximum976974
Zeros52
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-10-27T06:22:05.221838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile150051
Q1353380
median443115
Q3627750
95-th percentile674500
Maximum976974
Range976974
Interquartile range (IQR)274370

Descriptive statistics

Standard deviation177726.58
Coefficient of variation (CV)0.38770513
Kurtosis-1.0466921
Mean458406.58
Median Absolute Deviation (MAD)180156
Skewness-0.37630054
Sum2.4425049 × 1011
Variance3.1586738 × 1010
MonotonicityNot monotonic
2024-10-27T06:22:05.455149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
162600 1278
 
0.2%
623428 1128
 
0.2%
624480 1089
 
0.2%
398046 1077
 
0.2%
162609 966
 
0.2%
398036 954
 
0.2%
429965 849
 
0.2%
162626 842
 
0.2%
670000 820
 
0.2%
620050 733
 
0.1%
Other values (29026) 523089
98.2%
ValueCountFrequency (%)
0 52
< 0.1%
23 1
 
< 0.1%
27 1
 
< 0.1%
454 1
 
< 0.1%
2600 1
 
< 0.1%
2624 1
 
< 0.1%
4531 1
 
< 0.1%
6025 1
 
< 0.1%
6323 1
 
< 0.1%
11396 2
 
< 0.1%
ValueCountFrequency (%)
976974 1
< 0.1%
964620 1
< 0.1%
962036 1
< 0.1%
943250 1
< 0.1%
925220 2
< 0.1%
924930 1
< 0.1%
920050 1
< 0.1%
906012 1
< 0.1%
882081 1
< 0.1%
851690 1
< 0.1%

cprtn_prd_d
Real number (ℝ)

High correlation 

Distinct5848
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean324.07322
Minimum0
Maximum7269
Zeros551
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-10-27T06:22:05.671582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile87
Q1108
median182
Q3326
95-th percentile771
Maximum7269
Range7269
Interquartile range (IQR)218

Descriptive statistics

Standard deviation581.01115
Coefficient of variation (CV)1.7928392
Kurtosis44.227609
Mean324.07322
Median Absolute Deviation (MAD)87
Skewness6.1400166
Sum1.7267431 × 108
Variance337573.96
MonotonicityNot monotonic
2024-10-27T06:22:05.897839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 10411
 
2.0%
90 7386
 
1.4%
93 7273
 
1.4%
94 6834
 
1.3%
87 6731
 
1.3%
95 6662
 
1.3%
92 6525
 
1.2%
88 6268
 
1.2%
98 6145
 
1.2%
99 5736
 
1.1%
Other values (5838) 462854
86.9%
ValueCountFrequency (%)
0 551
0.1%
4 1
 
< 0.1%
14 1
 
< 0.1%
17 1
 
< 0.1%
23 1
 
< 0.1%
29 2
 
< 0.1%
32 5
 
< 0.1%
33 1
 
< 0.1%
35 1
 
< 0.1%
36 14
 
< 0.1%
ValueCountFrequency (%)
7269 1
< 0.1%
7260 1
< 0.1%
7252 1
< 0.1%
7249 1
< 0.1%
7247 1
< 0.1%
7238 2
< 0.1%
7236 1
< 0.1%
7231 1
< 0.1%
7219 1
< 0.1%
7215 1
< 0.1%

prsnt_age
Real number (ℝ)

High correlation 

Distinct62
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.142499
Minimum37
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-10-27T06:22:06.127526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile59
Q162
median64
Q366
95-th percentile70
Maximum99
Range62
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.6932793
Coefficient of variation (CV)0.057579286
Kurtosis1.4775444
Mean64.142499
Median Absolute Deviation (MAD)2
Skewness-0.062734683
Sum34176727
Variance13.640312
MonotonicityNot monotonic
2024-10-27T06:22:06.579221image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64 70967
13.3%
65 65047
12.2%
66 58031
10.9%
67 47351
8.9%
63 40049
7.5%
62 39590
7.4%
61 38324
7.2%
60 36023
6.8%
59 34879
6.5%
68 24172
 
4.5%
Other values (52) 78392
14.7%
ValueCountFrequency (%)
37 1
 
< 0.1%
39 1
 
< 0.1%
40 2
 
< 0.1%
41 2
 
< 0.1%
42 4
 
< 0.1%
43 2
 
< 0.1%
44 8
 
< 0.1%
45 15
 
< 0.1%
46 33
< 0.1%
47 74
< 0.1%
ValueCountFrequency (%)
99 2
 
< 0.1%
98 1
 
< 0.1%
97 1
 
< 0.1%
96 1
 
< 0.1%
95 2
 
< 0.1%
94 1
 
< 0.1%
93 4
< 0.1%
92 2
 
< 0.1%
91 8
< 0.1%
90 6
< 0.1%

pnsn_age
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.073424
Minimum55
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-10-27T06:22:06.769457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile55
Q155
median55
Q360
95-th percentile61
Maximum65
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.5669216
Coefficient of variation (CV)0.044975778
Kurtosis-1.0745384
Mean57.073424
Median Absolute Deviation (MAD)0
Skewness0.64548734
Sum30410147
Variance6.5890867
MonotonicityNot monotonic
2024-10-27T06:22:06.932833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
55 294227
55.2%
60 162647
30.5%
56 34852
 
6.5%
61 31149
 
5.8%
65 4923
 
0.9%
59 4765
 
0.9%
58 130
 
< 0.1%
63 77
 
< 0.1%
64 55
 
< 0.1%
ValueCountFrequency (%)
55 294227
55.2%
56 34852
 
6.5%
58 130
 
< 0.1%
59 4765
 
0.9%
60 162647
30.5%
61 31149
 
5.8%
63 77
 
< 0.1%
64 55
 
< 0.1%
65 4923
 
0.9%
ValueCountFrequency (%)
65 4923
 
0.9%
64 55
 
< 0.1%
63 77
 
< 0.1%
61 31149
 
5.8%
60 162647
30.5%
59 4765
 
0.9%
58 130
 
< 0.1%
56 34852
 
6.5%
55 294227
55.2%

accnt_bgn_year
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008.713
Minimum2004
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 MiB
2024-10-27T06:22:07.106786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2004
5-th percentile2005
Q12006
median2009
Q32011
95-th percentile2013
Maximum2023
Range19
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.8627334
Coefficient of variation (CV)0.001425158
Kurtosis-0.77361452
Mean2008.713
Median Absolute Deviation (MAD)2
Skewness0.25320765
Sum1.0702925 × 109
Variance8.1952426
MonotonicityNot monotonic
2024-10-27T06:22:07.295929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2006 75564
14.2%
2010 71690
13.5%
2005 64622
12.1%
2011 59284
11.1%
2007 55495
10.4%
2009 46483
8.7%
2013 44962
8.4%
2008 41979
7.9%
2012 40186
7.5%
2004 18691
 
3.5%
Other values (10) 13869
 
2.6%
ValueCountFrequency (%)
2004 18691
 
3.5%
2005 64622
12.1%
2006 75564
14.2%
2007 55495
10.4%
2008 41979
7.9%
2009 46483
8.7%
2010 71690
13.5%
2011 59284
11.1%
2012 40186
7.5%
2013 44962
8.4%
ValueCountFrequency (%)
2023 2
 
< 0.1%
2022 3
 
< 0.1%
2021 13
 
< 0.1%
2020 30
 
< 0.1%
2019 46
 
< 0.1%
2018 622
 
0.1%
2017 1233
 
0.2%
2016 2775
0.5%
2015 6405
1.2%
2014 2740
0.5%

erly_pnsn_flg
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.5 MiB
0
513448 
1
 
19377

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters532825
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 513448
96.4%
1 19377
 
3.6%

Length

2024-10-27T06:22:07.486706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-27T06:22:07.640702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 513448
96.4%
1 19377
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 513448
96.4%
1 19377
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 532825
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 513448
96.4%
1 19377
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 532825
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 513448
96.4%
1 19377
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 532825
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 513448
96.4%
1 19377
 
3.6%

Interactions

2024-10-27T06:21:57.361413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:28.576936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:31.050990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:33.382167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:37.571239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:41.797513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:44.504138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:47.068212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:49.514367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:52.277711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:54.952410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:57.576177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:28.848607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:31.259718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:33.610532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:37.897042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:42.164997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:44.717330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:47.276896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:49.753807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:52.471881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:55.220500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:57.782872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:29.062268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:31.460284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:33.905918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:38.305706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:42.492738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:44.948811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:47.497269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:50.023331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:52.678504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:55.508916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:58.012439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:29.293220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:31.676699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:34.251777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:38.689266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:42.748864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:45.181001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:47.743073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:50.276682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:52.892664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:55.725548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:58.230193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:29.496989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:31.873138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:35.381976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:39.151883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:42.952992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:45.389740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:47.966715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:50.488898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:53.103346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:55.929787image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:58.446338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:29.720527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:32.097804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:35.770627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:39.653386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:43.181418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:45.754308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:48.206494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:50.769614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:53.324718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:56.152575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:58.673090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:29.944067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:32.310060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:36.004310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:40.055189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:43.406240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:45.970923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:48.425073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:50.985992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:53.555718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:56.365995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:58.885972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:30.181681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:32.532611image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:36.289108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:40.451749image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:43.625129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:46.185024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:48.641752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:51.254119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:53.805428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:56.557303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:59.123556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:30.409220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:32.751891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:36.568727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:40.859089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:43.849936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:46.406130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:48.862323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:51.577637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:54.049768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:56.762776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:59.343644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:30.621390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:32.955998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:36.827562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:41.204257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:44.072659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:46.624275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:49.084718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:51.809240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:54.268512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:56.955939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:59.549328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:30.827458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:33.160838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:37.190124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:41.536047image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:44.277260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:46.830904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:49.284260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:52.048339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:54.732879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-27T06:21:57.147367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-27T06:22:07.762910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
accnt_bgn_dateaccnt_bgn_yearaddrss_typebrth_plccprtn_prd_derly_pnsn_flggndrlklocationokatopnsn_ageprsnt_ageprvs_npfpstl_code
accnt_bgn_date1.0000.9940.2560.0900.3470.4850.1730.2360.0590.019-0.076-0.2530.0950.023
accnt_bgn_year0.9941.0000.2570.0890.4190.4830.1740.2370.0580.017-0.076-0.2520.0940.030
addrss_type0.2560.2571.0000.0550.0690.0680.1030.0760.0260.019-0.041-0.0970.312-0.012
brth_plc0.0900.0890.0551.0000.0360.1400.0220.0700.1220.0900.007-0.0690.057-0.030
cprtn_prd_d0.3470.4190.0690.0361.0000.8060.0570.2470.020-0.0020.036-0.1800.0640.047
erly_pnsn_flg0.4850.4830.0680.1400.8061.0000.0370.2500.1570.0920.7210.6620.1850.066
gndr0.1730.1740.1030.0220.0570.0371.0000.0620.0180.0250.9630.4980.0710.024
lk0.2360.2370.0760.0700.2470.2500.0621.0000.0820.0480.1800.2480.1160.022
location0.0590.0580.0260.1220.0200.1570.0180.0821.0000.457-0.005-0.0680.061-0.057
okato0.0190.0170.0190.090-0.0020.0920.0250.0480.4571.0000.005-0.0430.046-0.012
pnsn_age-0.076-0.076-0.0410.0070.0360.7210.9630.180-0.0050.0051.0000.320-0.0200.010
prsnt_age-0.253-0.252-0.097-0.069-0.1800.6620.4980.248-0.068-0.0430.3201.000-0.0870.033
prvs_npf0.0950.0940.3120.0570.0640.1850.0710.1160.0610.046-0.020-0.0871.000-0.068
pstl_code0.0230.030-0.012-0.0300.0470.0660.0240.022-0.057-0.0120.0100.033-0.0681.000

Missing values

2024-10-27T06:21:59.806602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-27T06:22:00.555309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

locationaddrss_typeprvs_npfbrth_plcokatogndrlkaccnt_bgn_datepstl_codecprtn_prd_dprsnt_agepnsn_ageaccnt_bgn_yearerly_pnsn_flg
00.0363670.0363670.0363670.0363670.036367001135123200000000000644001.096645520050
10.0363670.0181830.0181830.0363670.036367101246233600000000000676852.0283706020090
20.0363670.0121220.0121220.0363670.036367101167004800000000000109451.088696020060
30.0363670.0090920.0363670.0363670.036367001378166400000000000423464.01301625520130
40.0363670.0363670.0090920.0363670.036367101291593600000000000427415.0106696020100
50.0363670.0072730.0072730.0363670.036367001354233600000000000623415.0116685520120
60.0363670.0060610.0060610.0363670.036367011311033600000000000636500.0253715520110
70.0363670.0051950.0051950.0363670.036367001155254400000000000633542.0224605520060
80.0363670.0181830.0181830.0363670.036367001239753600000000000453130.0358625520090
90.0363670.0045460.0045460.0363670.018183001196035200000000000624480.0123635520070
locationaddrss_typeprvs_npfbrth_plcokatogndrlkaccnt_bgn_datepstl_codecprtn_prd_dprsnt_agepnsn_ageaccnt_bgn_yearerly_pnsn_flg
5328150.0078990.0304520.0255180.0363670.026559001135036800000000000403876.097645520050
5328160.0259660.0304520.0404850.0011020.029911001385769600000000000420078.0544625520130
5328170.0034820.0304520.0255180.0002960.027301001142553600000000000457351.0371675520060
5328180.0070080.0304520.0255180.0140050.025558001132012800000000000632332.0132605520050
5328190.0069950.0304510.0255180.0051950.025556101150934400000000000632332.0274686020060
5328200.0382280.0304510.0255180.0493820.037016001387756800000000000603070.0490595620130
5328210.0372000.0304510.0255180.0051950.046430101211932800000000000185030.0307686020080
5328220.0304810.0304510.0255170.0363670.039656101292371200000000000452155.097656020100
5328230.0027440.0304510.0033060.0363670.010676001288742400000000000393761.0139655520100
5328240.0533440.0304510.0645110.0451330.056457101283126400000000000660064.0204656020100